Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns

Alsawalqah, Hamad; Hijazi, Neveen; Eshtay, Mohammed; Faris, Hossam; Radaideh, Ahmed Al; Aljarah, Ibrahim; Alshamaileh, Yazan

doi:10.3390/app10051745

Submit to this Journal Review for this Journal Edit a Special Issue

Article Menu

...

Open AccessArticle

Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns

¹,

¹,

²,

^1,*,

³,

and

King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan

Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarq 13132, Jordan

College of Humanities and Sciences, University of Science and Technology of Fujairah, Fujairah 2202, UAE

Author to whom correspondence should be addressed.

Appl. Sci. 2020, 10(5), 1745; https://doi.org/10.3390/app10051745

Received: 28 October 2019 / Revised: 20 February 2020 / Accepted: 25 February 2020 / Published: 3 March 2020

(This article belongs to the Section Computing and Artificial Intelligence)

View Full-Text Download PDF

Browse Figures

Citation Export

Abstract

Software defect prediction is a promising approach aiming to improve software quality and testing efficiency by providing timely identification of defect-prone software modules before the actual testing process begins. These prediction results help software developers to effectively allocate their limited resources to the modules that are more prone to defects. In this paper, a hybrid heterogeneous ensemble approach is proposed for the purpose of software defect prediction. Heterogeneous ensembles consist of set of classifiers of different learning base methods in which each of them has its own strengths and weaknesses. The main idea of the proposed approach is to develop expert and robust heterogeneous classification models. Two versions of the proposed approach are developed and experimented. The first is based on simple classifiers, and the second is based on ensemble ones. For evaluation, 21 publicly available benchmark datasets are selected to conduct the experiments and benchmark the proposed approach. The evaluation results show the superiority of the ensemble version over other well-regarded basic and ensemble classifiers. View Full-Text

Keywords: software defect prediction; ensembles; clustering; segmentation; classification

▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share and Cite

MDPI and ACS Style

Alsawalqah, H.; Hijazi, N.; Eshtay, M.; Faris, H.; Radaideh, A.A.; Aljarah, I.; Alshamaileh, Y. Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns. Appl. Sci. 2020, 10, 1745. https://doi.org/10.3390/app10051745

AMA Style

Alsawalqah H, Hijazi N, Eshtay M, Faris H, Radaideh AA, Aljarah I, Alshamaileh Y. Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns. Applied Sciences. 2020; 10(5):1745. https://doi.org/10.3390/app10051745

Chicago/Turabian Style

Alsawalqah, Hamad, Neveen Hijazi, Mohammed Eshtay, Hossam Faris, Ahmed Al Radaideh, Ibrahim Aljarah, and Yazan Alshamaileh. 2020. "Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns" Applied Sciences 10, no. 5: 1745. https://doi.org/10.3390/app10051745

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Menu

Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI